Physical sensors are widely used in many products, such as modern machines, to measure and monitor physical phenomena, such as temperature, speed, and emissions from motor vehicles. Physical sensors often take direct measurements of the physical phenomena and convert these measurements into measurement data to be further processed by control systems. Although physical sensors take direct measurements of the physical phenomena, physical sensors and associated hardware are often costly and, sometimes, unreliable. Further, when control systems rely on physical sensors to operate properly, a failure of a physical sensor may render such control systems inoperable. For example, the failure of a speed or timing sensor in an engine may result in shutdown of the engine entirely even if the engine itself is still operable.
Instead of direct measurements, virtual sensors are developed to process other various physically measured values and to produce values that were previously measured directly by physical sensors. For example, U.S. Pat. No. 5,386,373 (the '373 patent) issued to Keeler et al. on Jan. 31, 1995, discloses a virtual continuous emission monitoring system with sensor validation. The '373 patent uses a back propagation-to-activation model and a monte-carlo search technique to establish and optimize a computational model used for the virtual sensing system to derive sensing parameters from other measured parameters. However, such conventional techniques often fail to address inter-correlation between individual measured parameters, especially at the time of generation and/or optimization of computational models, or to correlate the other measured parameters to the sensing parameters.
Further, a modern machine may need multiple sensors to function properly. It may be difficult to decide which sensor function should be provided by a physical sensor, which sensor function should be provided by a virtual sensor, or which sensor function should be provided by a combination of a physical sensor and a virtual sensor. Moreover, it may be difficult to determine required precision and/or reliability of a particular physical sensor. A physical sensor with a high precision or high reliability, i.e., high quality, may be more expensive than a normal physical sensor. Using high quality sensors for all sensor functions may increase product cost significantly.
In other circumstances, a modern machine may be retrofitted to provide new functionalities on existing machines. The new functionalities may require new sensors to be installed on the existing machines, which may be practically impossible or may result in substantial cost for retrofitting with new hardware and software.
Methods and systems consistent with certain features of the disclosed systems are directed to solving one or more of the problems set forth above.